Ensuring sessional academic success by rethinking the place of academic development

There has been growing recognition of the contribution that Sessional Academics make to student learning in higher education; with recent studies concluding that around half Australian university teaching is now performed by casual staff [Red Report 2008; May, 2013]. However, sector-wide research and institutional audits continue to raise concerns about academic development and quality assurance, as well as the recognition and retention of Sessional Academics. In response, universities offer academic development programs. However, while such centrally offered programs are undoubtedly useful, they are necessarily generic and cannot address the local contexts of faculties or provide ‘on the ground’ support. This paper presents a new, distributed model of academic support and development for Sessional academics at Queensland University of Technology. Entitled the Sessional Academic Success program, it employs the principles of distributed leadership. Experienced Sessional academics are trained and supported to assume roles as Sessional Academic Success Advisors within their schools. Complementing our central programs, they design bespoke, locally situated, peer-to-peer academic development for new Sessional teachers; provide ‘just in time’, safe and reliable advice; and build supportive communities of teaching practice in their local contexts. This distributed model re-envisages the forms and places of academic development and support. It helps ensure that new Sessional Academics are embraced by faculty life. And, recognizing that experienced Sessional Academics have much to contribute to the advancement of learning and teaching, it builds their capacity through leadership opportunities. As the designer/facilitator of the program and a Sessional Academic Success Advisor, the authors take a dialogic approach and together describe the design, implementation and outcomes of the program.

The Sessional Academic Success (SAS) project

The Sessional Academic Success (SAS) project is a sustainable, distributed model for supporting sessional staff at QUT. Developed by the Learning and Teaching Unit. SAS complements our Sessional Academic Program (SAP): a sequence of formal academic development workshops explained in complementary nomination. SAS recognises that while these programs are very well received and a crucial aspect of preparing and advancing sessional teachers, they are necessarily encapsulated in the moment of their delivery and are generic, as they address all faculties (with their varied cultures, processes and pedagogies). The SAS project extends this formal, centrally offered activity into local, ‘just in time’, ongoing support within schools. It takes a distributed leadership approach. Experienced sessional academics are recruited and employed as Sessional Academic Success Advisors (SASAs). They provide sessional staff in their schools with contextually specific, needs based, peer-to-peer development opportunities; one-on-one advice on classroom management and strategies for success; and help to trouble-shoot challenges. The SASAs are trained by the Learning and Teaching Unit co-ordinator, and ongoing support is provided centrally and by school-based co-ordinators. This team approach situates the SASAs at the centre of an organisation map (see diagram of support relationships below). The SAS project aims to support sessional staff in their professional development by: • Offering contextual, needs-based support at school level by harnessing local expertise; • Providing further development opportunities that are local and focal; SAS aims to retain Sessional Staff by: • Responding to self-nominated requests for support and ‘just in time’, safe and reliable advice in times of need; • Building sessional staff confidence through help with dealing with challenges from a trusted peer; • Building a supportive academic community for sessional staff, which helps them feel a part of faculty life, and a community of teaching practice. SAS aims to support sessional staff in the development of academic teaching careers by: • Recognising the capacity of experienced sessional staff to support their peers in ways that are unique, valuable and valued and providing the agency to do so; • Providing career advancement and leadership opportunities for sessional staff. SAS takes unique approaches within each school using strategies such as: • Welcomes and schools orientation by SASAs; • Regular check ins; face-to-face advice and online support; • Compiling local resources to complement university wide resources. • Sessional-to-sessional ‘just in time’ training (eg. assessment and marking when marking commences); • Peer feedback and mentoring (the opportunities to sit in more experiences sessionals’ classes; • Sessional staff awards (nominated by students); • Communities of practice to discuss topics and issues with a view to (and support for) publishing on learning and teaching. In these ways, SASAs complement support offered by unit coordinators, administrators, and the Learning and Teaching Unit. Pairing senior and ‘understudy’ advisors ensures a line of succession, sustainability and continuity. A pilot program commenced in 2012 involving three schools (Psychology and Social Work; Electrical Engineering and Computer Science; Media, Entertainment and Creative Arts). It will be expanded across schools in 2013.

A grouping model for distributed pipeline assets maintenance decision

Distributed pipeline assets systems are crucial to society. The deterioration of these assets and the optimal allocation of limited budget for their maintenance correspond to crucial challenges for water utility managers. Decision makers should be assisted with optimal solutions to select the best maintenance plan concerning available resources and management strategies. Much research effort has been dedicated to the development of optimal strategies for maintenance of water pipes. Most of the maintenance strategies are intended for scheduling individual water pipe. Consideration of optimal group scheduling replacement jobs for groups of pipes or other linear assets has so far not received much attention in literature. It is a common practice that replacement planners select two or three pipes manually with ambiguous criteria to group into one replacement job. This is obviously not the best solution for job grouping and may not be cost effective, especially when total cost can be up to multiple million dollars. In this paper, an optimal group scheduling scheme with three decision criteria for distributed pipeline assets maintenance decision is proposed. A Maintenance Grouping Optimization (MGO) model with multiple criteria is developed. An immediate challenge of such modeling is to deal with scalability of vast combinatorial solution space. To address this issue, a modified genetic algorithm is developed together with a Judgment Matrix. This Judgment Matrix is corresponding to various combinations of pipe replacement schedules. An industrial case study based on a section of a real water distribution network was conducted to test the new model. The results of the case study show that new schedule generated a significant cost reduction compared with the schedule without grouping pipes.

The impact of temperature on mortality in Tianjin, China : a case-crossover design with a distributed lag non-linear model

Background There has been increasing interest in assessing the impacts of temperature on mortality. However, few studies have used a case–crossover design to examine non-linear and distributed lag effects of temperature on mortality. Additionally, little evidence is available on the temperature-mortality relationship in China, or what temperature measure is the best predictor of mortality. Objectives To use a distributed lag non-linear model (DLNM) as a part of case–crossover design. To examine the non-linear and distributed lag effects of temperature on mortality in Tianjin, China. To explore which temperature measure is the best predictor of mortality; Methods: The DLNM was applied to a case¬−crossover design to assess the non-linear and delayed effects of temperatures (maximum, mean and minimum) on deaths (non-accidental, cardiopulmonary, cardiovascular and respiratory). Results A U-shaped relationship was consistently found between temperature and mortality. Cold effects (significantly increased mortality associated with low temperatures) were delayed by 3 days, and persisted for 10 days. Hot effects (significantly increased mortality associated with high temperatures) were acute and lasted for three days, and were followed by mortality displacement for non-accidental, cardiopulmonary, and cardiovascular deaths. Mean temperature was a better predictor of mortality (based on model fit) than maximum or minimum temperature. Conclusions In Tianjin, extreme cold and hot temperatures increased the risk of mortality. Results suggest that the effects of cold last longer than the effects of heat. It is possible to combine the case−crossover design with DLNMs. This allows the case−crossover design to flexibly estimate the non-linear and delayed effects of temperature (or air pollution) whilst controlling for season.

Unusual scene detection using distributed behaviour model and sparse representation

The ability to detect unusual events in surviellance footage as they happen is a highly desireable feature for a surveillance system. However, this problem remains challenging in crowded scenes due to occlusions and the clustering of people. In this paper, we propose using the Distributed Behavior Model (DBM), which has been widely used in computer graphics, for video event detection. Our approach does not rely on object tracking, and is robust to camera movements. We use sparse coding for classification, and test our approach on various datasets. Our proposed approach outperforms a state-of-the-art work which uses the social force model and Latent Dirichlet Allocation.

Fundamental solution and discrete random walk model for time-space fractional diffusion equation

In this paper, we consider a time-space fractional diffusion equation of distributed order (TSFDEDO). The TSFDEDO is obtained from the standard advection-dispersion equation by replacing the first-order time derivative by the Caputo fractional derivative of order α∈(0,1], the first-order and second-order space derivatives by the Riesz fractional derivatives of orders β 1∈(0,1) and β 2∈(1,2], respectively. We derive the fundamental solution for the TSFDEDO with an initial condition (TSFDEDO-IC). The fundamental solution can be interpreted as a spatial probability density function evolving in time. We also investigate a discrete random walk model based on an explicit finite difference approximation for the TSFDEDO-IC.

Probabilistic world modeling for distributed team planning

This paper describes an application of decoupled probabilistic world modeling to achieve team planning. The research is based on the principle that the action selection mechanism of a member in a robot team can select an effective action if a global world model is available to all team members. In the real world, the sensors are imprecise, and are individual to each robot, hence providing each robot a partial and unique view about the environment. We address this problem by creating a probabilistic global view on each agent by combining the perceptual information from each robot. This probabilistic view forms the basis for selecting actions to achieve the team goal in a dynamic environment. Experiments have been carried out to investigate the effectiveness of this principle using custom-built robots for real world performance, in addition, to extensive simulation results. The results show an improvement in team effectiveness when using probabilistic world modeling based on perception sharing for team planning.

Towards developing an integrated model of information behaviour

This paper presents the results from a study of information behaviors in the context of people's everyday lives undertaken in order to develop an integrated model of information behavior (IB). 34 participants from across 6 countries maintained a daily information journal or diary – mainly through a secure web log – for two weeks, to an aggregate of 468 participant days over five months. The text-rich diary data was analyzed using a multi-method qualitative-quantitative analysis in the following order: Grounded Theory analysis with manual coding, automated concept analysis using thesaurus-based visualization, and finally a statistical analysis of the coding data. The findings indicate that people engage in several information behaviors simultaneously throughout their everyday lives (including home and work life) and that sense-making is entangled in all aspects of them. Participants engaged in many of the information behaviors in a parallel, distributed, and concurrent fashion: many information behaviors for one information problem, one information behavior across many information problems, and many information behaviors concurrently across many information problems. Findings indicate also that information avoidance – both active and passive avoidance – is a common phenomenon and that information organizing behaviors or the lack thereof caused the most problems for participants. An integrated model of information behaviors is presented based on the findings.

Detection and prevention of distributed denial of services attacks by collaborative effort of software agents, first prototype implementation

Distributed Denial of Services DDoS, attacks has become one of the biggest threats for resources over Internet. Purpose of these attacks is to make servers deny from providing services to legitimate users. These attacks are also used for occupying media bandwidth. Currently intrusion detection systems can just detect the attacks but cannot prevent / track the location of intruders. Some schemes also prevent the attacks by simply discarding attack packets, which saves victim from attack, but still network bandwidth is wasted. In our opinion, DDoS requires a distributed solution to save wastage of resources. The paper, presents a system that helps us not only in detecting such attacks but also helps in tracing and blocking (to save the bandwidth as well) the multiple intruders using Intelligent Software Agents. The system gives dynamic response and can be integrated with the existing network defense systems without disturbing existing Internet model. We have implemented an agent based networking monitoring system in this regard.

Effects of transportation accessibility on residential property values: Application of spatial hedonic price model in Seoul, South Korea, metropolitan area

A number of studies have focused on estimating the effects of accessibility on housing values by using the hedonic price model. In the majority of studies, estimation results have revealed that housing values increase as accessibility improves, although the magnitude of estimates has varied across studies. Adequately estimating the relationship between transportation accessibility and housing values is challenging for at least two reasons. First, the monocentric city assumption applied in location theory is no longer valid for many large or growing cities. Second, rather than being randomly distributed in space, housing values are clustered in space—often exhibiting spatial dependence. Recognizing these challenges, a study was undertaken to develop a spatial lag hedonic price model in the Seoul, South Korea, metropolitan region, which includes a measure of local accessibility as well as systemwide accessibility, in addition to other model covariates. Although the accessibility measures can be improved, the modeling results suggest that the spatial interactions of apartment sales prices occur across and within traffic analysis zones, and the sales prices for apartment communities are devalued as accessibility deteriorates. Consistent with findings in other cities, this study revealed that the distance to the central business district is still a significant determinant of sales price.

A prioritised fuzzy constraint satisfaction approach to model agent negotiation for railway scheduling

With the recent regulatory reforms in a number of countries, railways resources are no longer managed by a single party but are distributed among different stakeholders. To facilitate the operation of train services, a train service provider (SP) has to negotiate with the infrastructure provider (IP) for a train schedule and the associated track access charge. This paper models the SP and IP as software agents and the negotiation as a prioritized fuzzy constraint satisfaction (PFCS) problem. Computer simulations have been conducted to demonstrate the effects on the train schedule when the SP has different optimization criteria. The results show that by assigning different priorities on the fuzzy constraints, agents can represent SPs with different operational objectives.

Managing process model collections with AProMoRe

As organizations reach higher levels of Business Process Management maturity, they tend to collect numerous business process models. Such models may be linked with each other or mutually overlap, supersede one another and evolve over time. Moreover, they may be represented at different abstraction levels depending on the target audience and modeling purpose, and may be available in multiple languages (e.g. due to company mergers). Thus, it is common that organizations struggle with keeping track of their process models. This demonstration introduces AProMoRe (Advanced Process Model Repository) which aims to facilitate the management of (large) process model collections.